Generally in the art, a superpixel is a polygonal cluster of pixels in a digital image, larger than a single pixel, which can be rendered in the same color and brightness, see U.S. Pat. No. 7,744,185
Superpixel segmentation is used in object recognition, image segmentation, and 3D reconstruction applications. One major advantage of using superpixels is computational efficiency. Superpixel representation greatly reduces the number of image primitives required, when compared with pixel representation.
For example, in an L-label labeling problem, the solution space for pixel representation is Ln where n is the number of pixels, typically 106. However, the solution space for superpixel representation is Lm where m is the number of superpixels, typically 102.
It is commonly assumed that a superpixel is a set of pixels from a single object. This leads to a practical definition of superpixel segmentation, which pixels partitions in images into perceptually consistent clusters. The perceptual consistent property implies superpixel boundaries preserve object boundaries.
Most clustering processes can be characterized as superpixel segmentation. However, most of conventional processes model general aspects of clusters, and are not optimized for superpixel segmentation. Besides, many processes require intensive computations, and are unsuitable for segmentations.
One method uses a graph-based superpixel segmentation. Images are mapped into a neighborhood graph. The method uses a boundary predicate to sequentially cut edges for constructing the superpixels. Although the method is fast, it produces superpixels with irregular shapes and sizes.
A mean-shift method is accurate for local variations, but it also suffers from the irregular superpixel problem, see U.S. Publication 20100284607.
Another method for superpixel segmentation is NCut, see U.S. Publication 20110013837. NCut, produces superpixels with similar size and compact shape. However, it is also computationally expensive requiring several minutes even for a moderate size image, e.g., 481×321 pixels.
TurboPixel is as an efficient alternative to achieve similar regularity. TurboPixel is based on evolving curve from seeds uniformly placed in the image. It uses various constraints during curve evolution to enforce superpixel regularity.
Graph cuts can be used to achieve regular superpixels through a dense patch assignment technique. In another method, the superpixel conforms to a regular grid using a probabilistic boundary map for defining cut cost. The objective used there allows isomorphism among images.